Some new ideas in nonparametric estimation

In the framework of an abstract statistical model we discuss how to use the solution of one estimation problem ({\it Problem A}) in order to construct an estimator in another, completely different, {\it Problem B}. As a solution of {\it Problem A} we understand a data-driven selection from a given family of estimators $\mathbf{A}(\mH)=\big\{\widehat{A}_\mh, \mh\in\mH\big\}$ and establishing for the selected estimator so-called oracle inequality. %parameterized by some se t$\mH$. If $\hat{\mh}\in\mH$ is the selected parameter and $\mathbf{B}(\mH)=\big\{\widehat{B}_\mh, \mh\in\mH\big\}$ is an estimator's collection built in {\it Problem B} we suggest to use the estimator $\widehat{B}_{\hat{\mh}}$. We present very general selection rule led to selector $\hat{\mh}$ and find conditions under which the estimator $\widehat{B}_{\hat{\mh}}$ is reasonable. Our approach is illustrated by several examples related to adaptive estimation.

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